Recursive least mean square algorithm based on fourth-order statistics

Ran Tao; Wei Qiang Zhang; Yong Feng Ma

Recursive least mean square algorithm based on fourth-order statistics

Ran Tao^*
, Wei Qiang Zhang
, Yong Feng Ma

^*Corresponding author for this work

School of Information and Electronics

Beijing Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.

Original language	English
Pages (from-to)	442-445
Number of pages	4
Journal	Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology
Volume	24
Issue number	5
Publication status	Published - May 2004

Keywords

High-order statistics
LMS algorithm
Recursive form

Cite this

@article{dae192b3c9884eb18bf9163377402d77,

title = "Recursive least mean square algorithm based on fourth-order statistics",

abstract = "An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.",

keywords = "High-order statistics, LMS algorithm, Recursive form",

author = "Ran Tao and Zhang, \{Wei Qiang\} and Ma, \{Yong Feng\}",

year = "2004",

month = may,

language = "English",

volume = "24",

pages = "442--445",

journal = "Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology",

issn = "1001-0645",

publisher = "Beijing Institute of Technology",

number = "5",

}

TY - JOUR

T1 - Recursive least mean square algorithm based on fourth-order statistics

AU - Tao, Ran

AU - Zhang, Wei Qiang

AU - Ma, Yong Feng

PY - 2004/5

Y1 - 2004/5

N2 - An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.

AB - An LMS algorithm based on one dimensional slice of fourth-order statistics is proposed and its recursive form is given. This algorithm can suppress the correlated Gaussian noise effectively, and its performance is better than the conventional autocorrelation-based LMS algorithm. The recursive form reduces the computational complexity, and it can meet the need of real-time processing. Simulation results are presented to demonstrate the effectiveness of this approach. The algorithm can be used to estimate multi-path coefficients in radar, sonar and communication systems.

KW - High-order statistics

KW - LMS algorithm

KW - Recursive form

UR - https://www.scopus.com/pages/publications/4344603864

M3 - Article

AN - SCOPUS:4344603864

SN - 1001-0645

VL - 24

SP - 442

EP - 445

JO - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

JF - Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology

IS - 5

ER -

Recursive least mean square algorithm based on fourth-order statistics

Abstract

Keywords

Other files and links

Fingerprint

Cite this